CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance
Abstract Accurately estimating the state-of-charge (SOC) of lithium-ion batteries is of great significance for the energy management and range calculation of electric vehicles. With the development of graphics processing units, SOC estimation based on data-driven methods, especially using recurrent...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15597-2 |
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| _version_ | 1849332983032446976 |
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| author | Zhaowei Zhang Chen Liu Tianyu Li Tian Wang Yaoyao Cui Pengcheng Zhao |
| author_facet | Zhaowei Zhang Chen Liu Tianyu Li Tian Wang Yaoyao Cui Pengcheng Zhao |
| author_sort | Zhaowei Zhang |
| collection | DOAJ |
| description | Abstract Accurately estimating the state-of-charge (SOC) of lithium-ion batteries is of great significance for the energy management and range calculation of electric vehicles. With the development of graphics processing units, SOC estimation based on data-driven methods, especially using recurrent neural networks, has received considerable attention in recent years. However, existing data-driven methods often neglect internal resistance, which is highly detrimental to the accuracy of SOC estimation. In addition, commonly used network optimization algorithms do not always maximize the convergence speed and performance simultaneously. To solve these problems, this paper describes a battery test bench for producing an effective lithium-ion battery dataset containing current, voltage, temperature, and more importantly, internal resistance measurements. To improve the estimated SOC performance, the internal resistance is considered in the construction of a data-driven model. Using a convolutional neural network (CNN) and long short-term memory (LSTM), we propose an optimization model that switches from Adam to stochastic gradient descent (SWATS). A well-known public battery dataset and an experimentally measured dataset are used to verify the feasibility of the SWATS scheme. The results show that, compared with existing data-driven methods, the proposed method is effective, especially in terms of robustness and generalization. |
| format | Article |
| id | doaj-art-971176ee3ef34cfda5937e1c0cc1637a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-971176ee3ef34cfda5937e1c0cc1637a2025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-08-0115111610.1038/s41598-025-15597-2CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistanceZhaowei Zhang0Chen Liu1Tianyu Li2Tian Wang3Yaoyao Cui4Pengcheng Zhao5School of Mechanical and Electrical Engineering, Shijiazhuang UniversitySchool of Mechanical and Electrical Engineering, CSPC Zhongnuo Pharmaceutical (Shijiazhuang) Co., Ltd.,School of Mechanical and Electrical Engineering, Wooking Scientific Instrument Co., Ltd.,School of Mechanical and Electrical Engineering, Shijiazhuang UniversitySchool of Mechanical and Electrical Engineering, Shijiazhuang UniversitySchool of Mechanical and Electrical Engineering, Shijiazhuang UniversityAbstract Accurately estimating the state-of-charge (SOC) of lithium-ion batteries is of great significance for the energy management and range calculation of electric vehicles. With the development of graphics processing units, SOC estimation based on data-driven methods, especially using recurrent neural networks, has received considerable attention in recent years. However, existing data-driven methods often neglect internal resistance, which is highly detrimental to the accuracy of SOC estimation. In addition, commonly used network optimization algorithms do not always maximize the convergence speed and performance simultaneously. To solve these problems, this paper describes a battery test bench for producing an effective lithium-ion battery dataset containing current, voltage, temperature, and more importantly, internal resistance measurements. To improve the estimated SOC performance, the internal resistance is considered in the construction of a data-driven model. Using a convolutional neural network (CNN) and long short-term memory (LSTM), we propose an optimization model that switches from Adam to stochastic gradient descent (SWATS). A well-known public battery dataset and an experimentally measured dataset are used to verify the feasibility of the SWATS scheme. The results show that, compared with existing data-driven methods, the proposed method is effective, especially in terms of robustness and generalization.https://doi.org/10.1038/s41598-025-15597-2State-of-chargeSwitchesInternal resistanceLithium-ion battery |
| spellingShingle | Zhaowei Zhang Chen Liu Tianyu Li Tian Wang Yaoyao Cui Pengcheng Zhao CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance Scientific Reports State-of-charge Switches Internal resistance Lithium-ion battery |
| title | CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance |
| title_full | CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance |
| title_fullStr | CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance |
| title_full_unstemmed | CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance |
| title_short | CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance |
| title_sort | cnn lstm optimized with swats for accurate state of charge estimation in lithium ion batteries considering internal resistance |
| topic | State-of-charge Switches Internal resistance Lithium-ion battery |
| url | https://doi.org/10.1038/s41598-025-15597-2 |
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